Supervised Gradual Machine Learning for Efficient Aspect Category Detection in Review Sentences
The core message of this paper is to propose a novel supervised Gradual Machine Learning (GML) approach that leverages the strength of Deep Neural Networks (DNNs) in semantic relation modeling to enable effective knowledge conveyance for the task of Aspect Category Detection (ACD).
Enhancing Aspect-Based Sentiment Analysis Systems through Edge-Enhanced Graph Convolutional Networks
The core message of this study is to enhance the performance of aspect-based sentiment analysis systems by leveraging an edge-enhanced bidirectional graph convolutional network (Bi-GCN) called SentiSys. SentiSys combines Bi-LSTM, a transformer encoder, and Bi-GCN to effectively capture syntactic dependencies, global contextual information, and aspect-specific sentiment features.
Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation: A Novel Approach for Improved Aspect-Based Sentiment Analysis
Aspect sentiment coherency is a prevalent yet underexplored phenomenon in aspect-based sentiment analysis, where adjacent aspects often share similar sentiments. This work proposes a novel local sentiment aggregation (LSA) paradigm to effectively model aspect sentiment coherency, leading to significant improvements in aspect sentiment classification performance.
